AlphaStar and other complex problems

While StarCraft is just a game, albeit a complex one, we think that the techniques behind AlphaStar could be useful in solving other problems. For example, its neural network architecture is capable of modelling very long sequences of likely actions – with games often lasting up to an hour with tens of thousands of moves – based on imperfect information. Each frame of StarCraft is used as one step of input, with the neural network predicting the expected sequence of actions for the rest of the game after every frame. The fundamental problem of making complex predictions over very long sequences of data appears in many real world challenges, such as weather prediction, climate modelling, language understanding and more. We’re very excited about the potential to make significant advances in these domains using learnings and developments from the AlphaStar project.

We also think some of our training methods may prove useful in the study of safe and robust AI. One of the great challenges in AI is the number of ways in which systems could go wrong, and StarCraft pros have previously found it easy to beat AI systems by finding inventive ways to provoke these mistakes. AlphaStar’s innovative league-based training process finds the approaches that are most reliable and least likely to go wrong. We’re excited by the potential for this kind of approach to help improve the safety and robustness of AI systems in general, particularly in safety-critical domains like energy, where it’s essential to address complex edge cases.

Achieving the highest levels of StarCraft play represents a major breakthrough in one of the most complex video games ever created. We believe that these advances, alongside other recent progress in projects such as AlphaZero and AlphaFold, represent a step forward in our mission to create intelligent systems that will one day help us unlock novel solutions to some of the world’s most important and fundamental scientific problems.

We are thankful for the support and immense skill of Team Liquid’s TLO and MaNa. We are also grateful for the continued support of Blizzard and the StarCraft community for making this work possible.

AlphaStar Team:

Oriol Vinyals, Igor Babuschkin, Junyoung Chung, Michael Mathieu, Max Jaderberg, Wojtek Czarnecki, Andrew Dudzik, Aja Huang, Petko Georgiev, Richard Powell, Timo Ewalds, Dan Horgan, Manuel Kroiss, Ivo Danihelka, John Agapiou, Junhyuk Oh, Valentin Dalibard, David Choi, Laurent Sifre, Yury Sulsky, Sasha Vezhnevets, James Molloy, Trevor Cai, David Budden, Tom Paine, Caglar Gulcehre, Ziyu Wang, Tobias Pfaff, Toby Pohlen, Yuhuai Wu, Dani Yogatama, Julia Cohen, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy Lillicrap, Chris Apps, Koray Kavukcuoglu, Demis Hassabis, David Silver

With thanks to:

Ali Razavi, Daniel Toyama, David Balduzzi, Doug Fritz, Eser Aygün, Florian Strub, Guillaume Alain, Haoran Tang, Jaume Sanchez, Jonathan Fildes, Julian Schrittwieser, Justin Novosad, Karen Simonyan, Karol Kurach, Philippe Hamel, Remi Leblond, Ricardo Barreira, Scott Reed, Sergey Bartunov, Shibl Mourad, Steve Gaffney, Thomas Hubert, the team that created PySC2 and the whole DeepMind Team, with special thanks to the research platform team, comms and events teams.

Source: https://deepmind.com/blog/article/alphastar-mastering-real-time-strategy-game-starcraft-ii